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A nomogram to predict rupture risk of middle cerebral artery aneurysm

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Abstract

Background

Determining the rupture risk of unruptured intracranial aneurysm is crucial for treatment strategy. The purpose of this study was to predict the rupture risk of middle cerebral artery (MCA) aneurysms using a machine learning technique.

Methods

We retrospectively reviewed 403 MCA aneurysms and randomly partitioned them into the training and testing datasets with a ratio of 8:2. A generalized linear model with logit link was developed using training dataset to predict the aneurysm rupture risk based on the clinical variables and morphological features manually measured from computed tomography angiography. To facilitate the clinical application, we further constructed an easy-to-use nomogram based on the developed model.

Results

Ruptured MCA aneurysm had larger aneurysm size, aneurysm height, perpendicular height, aspect ratio, size ratio, bottleneck factor, and height-width ratio. Presence of a daughter-sac was more common in ruptured than in unruptured MCA aneurysms. Six features, including aneurysm multiplicity, lobulations, size ratio, bottleneck factor, height-width ratio, and aneurysm angle, were adopted in the model after feature selection. The model achieved a relatively good performance with areas under the receiver operating characteristic curves of 0.77 in the training dataset and 0.76 in the testing dataset. The nomogram provided a visual interpretation of our model, and the rupture risk probability of MCA aneurysms can be directly read from it.

Conclusion

Our model can be used to predict the rupture risk of MCA aneurysm.

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Data Availability

The datasets analyzed during the current study are available from the corresponding author on a reasonable request.

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Funding

This study was supported by the Basic Research Project of Wenzhou (Y2020166), Wenzhou Major Program of Science and Technology Innovation (ZY2020012), Health Foundation for Creative Talents in Zhejiang Province, China (No.2016), and Natural Science Foundation of Zhejiang Province, China (Grant No. LQ15H180002).

Author information

Authors and Affiliations

  1. Department of Radiology, The First Affiliated Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, 325000, China

    Jinjin Liu, Yongchun Chen, Dongqin Zhu, Qiong Li, Zhonggang Chen, Jiafeng Zhou, Boli Lin, Yunjun Yang & Xiufen Jia

Authors
  1. Jinjin Liu
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  2. Yongchun Chen
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  3. Dongqin Zhu
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  4. Qiong Li
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  5. Zhonggang Chen
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  7. Boli Lin
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  8. Yunjun Yang
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  9. Xiufen Jia
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Contributions

Jinjin Liu, Xiufen Jia, and Yunjun Yang contributed to the study conception and design. Material preparation, data collection, and analysis were performed by Jinjin Liu, Xiufen Jia, Jiafeng Zhou, Boli Lin, Dongqing Zhu, Qiong Li, and Zhonggang Chen. The first draft of the manuscript was written by Jinjin Liu, and all authors commented on previous versions of the manuscript. All authors read and approved the final manuscript.

Corresponding authors

Correspondence to Yunjun Yang or Xiufen Jia.

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The authors declare no competing interests.

Ethical approval

This study was approved by the ethical committee of the First Affiliated Hospital of Wenzhou Medical University.

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Informed consent was obtained from all patients involved in the study.

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Liu, J., Chen, Y., Zhu, D. et al. A nomogram to predict rupture risk of middle cerebral artery aneurysm. Neurol Sci 42, 5289–5296 (2021). https://doi.org/10.1007/s10072-021-05255-6

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  • DOI: https://doi.org/10.1007/s10072-021-05255-6

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